aclnnCtcLossBackward
Applicable Products
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Function
Performs backward propagation of aclnnCtcLoss to compute the CTC loss gradient.
Function Prototype
Each operator is divided into a two-phase API. First call the "aclnnCtcLossBackwardGetWorkspaceSize" API to obtain the workspace size required for computation and the executor that contains the operator computation process, and then call the "aclnnCtcLossBackward" API to perform the computation.
aclnnStatus aclnnCtcLossBackwardGetWorkspaceSize(
const aclTensor* gradOut,
const aclTensor* logProbs,
const aclTensor* targets,
const aclIntArray* inputLengths,
const aclIntArray* targetLengths,
const aclTensor* negLogLikelihood,
const aclTensor* logAlpha,
int64_t blank,
bool zeroInfinity,
aclTensor* out,
uint64_t* workspaceSize,
aclOpExecutor** executor)aclnnStatus aclnnCtcLossBackward(
void* workspace,
uint64_t workspaceSize,
aclOpExecutor* executor,
aclrtStream stream)aclnnCtcLossBackwardGetWorkspaceSize
Parameters:
Parameter Input/Output Description Instruction Data Type Data Format Dimension (shape) Non-contiguous Tensor gradOut (aclTensor*) Input Represents the gradient update coefficient. The data type must be consistent with logProbs. FLOAT16, BFLOAT16, FLOAT, DOUBLE ND N √ logProbs (aclTensor*) Input Represents the logarithmic probability of the output. - FLOAT16, BFLOAT16, FLOAT, DOUBLE ND (T, N, C)
T is the input length, N is the batch size, and C is the number of classes, including the blank label.√ targets (aclTensor*) Input Represents the labels containing the target sequence. When the shape is (N, S), S is a value not less than the maximum value in targetLengths; or when the shape is (SUM(targetLengths)), it is assumed that targets are unpadded and concatenated within one dimension. The value must be less than C and greater than or equal to 0. INT64, INT32 ND - √ inputLengths（aclIntArray*） Input Indicates the actual length of the input sequence. The array length is N, and each value in the array must be less than or equal to T. INT64, INT32 - - - targetLengths (aclIntArray*) Input Represents the actual lengths of the target sequences. The array length is N. When the shape of targets is (N, S), each value in the array must be less than or equal to S, with the maximum value being maxTargetLength. INT64, INT32 - - - negLogLikelihood (aclTensor*) Input Represents the differentiable loss value relative to each input node. The data type must be consistent with logProbs. Consistent with logProbs. ND - √ logAlpha (aclTensor*) Input Represents the probability of possible traces from input to target. Must be consistent with logProbs in data type. Must be a non-empty 3D tensor with shape (N, T, alpha), satisfying (alpha − 1) / 2 >= maxTargetLength. Consistent with logProbs. ND - √ blank (int64_t) Input Indicates the blank label. The value must be less than C and greater than or equal to 0. - - - - zeroInfinity (bool) Input Indicates whether to zero out infinite losses and associated gradients. - - - - - out (aclTensor*) Output Represents the loss gradient of CTC. The data type must be consistent with gradOut. Consistent with gradOut. ND (T,N,C) √ workspaceSize (uint64_t*) Output Returns the workspace size to be applied for on the Device side. - - - - - executor (aclOpExecutor**) Output Returns the op executor, which contains the operator computation flow. - - - - - Return value
aclnnStatus: return code. For details, see aclnn Return Code.
The first-phase API performs input parameter validation and returns an error in the following scenarios:
Return Value Error Code Description ACLNN_ERR_PARAM_NULLPTR 161001 When the passed gradOut, logProbs, targets, inputLengths, targetLengths, negLogLikelihood, logAlpha, or out is a null pointer. ACLNN_ERR_PARAM_INVALID 161002 The data type of gradOut, logProbs, targets, inputLengths, targetLengths, negLogLikelihood, logAlpha, or out is not within the supported range. The data type of gradOut, negLogLikelihood, logAlpha, or out differs from that of logProbs. The tensors gradOut, logProbs, targets, negLogLikelihood, logAlpha, and out do not meet the corresponding shape requirements, or the lengths of the ArrayLists inputLengths and targetLengths do not meet the requirements. blank does not meet the value range requirement.
aclnnCtcLossBackward
Parameters:
Parameter Input/Output Description workspace Input Memory address of the workspace applied for on the Device side. workspaceSize Input Size of the workspace applied for on the Device side, obtained from the first-phase API aclnnCtcLossBackwardGetWorkspaceSize. executor Input Operator executor, which contains the operator computation flow. stream Input Specifies the stream that executes the task. Return value
aclnnStatus: return code. For details, see aclnn Return Code.
Constraints
None.
Example
The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_ctc_loss_backward.h"
#define CHECK_RET(cond, return_expr) \
do { \
if (!(cond)) { \
return_expr; \
} \
} while (0)
#define LOG_PRINT(message, ...) \
do { \
printf(message, ##__VA_ARGS__); \
} while (0)
int64_t GetShapeSize(const std::vector<int64_t>& shape) {
int64_t shape_size = 1;
for (auto i : shape) {
shape_size *= i;
}
return shape_size;
}
int Init(int32_t deviceId, aclrtStream* stream) {
// Boilerplate for resource initialization.
auto ret = aclInit(nullptr);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
ret = aclrtSetDevice(deviceId);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
ret = aclrtCreateStream(stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
return 0;
}
template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
aclDataType dataType, aclTensor** tensor) {
auto size = GetShapeSize(shape) * sizeof(T);
// Call aclrtMalloc to allocate device-side memory.
auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
// Call aclrtMemcpy to copy data from the host to the device memory.
ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);
// Calculate the strides of a contiguous tensor.
std::vector<int64_t> strides(shape.size(), 1);
for (int64_t i = shape.size() - 2; i >= 0; i--) {
strides[i] = shape[i + 1] * strides[i + 1];
}
// Call the aclCreateTensor API to create an aclTensor.
*tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
shape.data(), shape.size(), *deviceAddr);
return 0;
}
int main() {
// 1. (Boilerplate) Initialize the device/stream. For details, see the acl API manual.
// Set deviceId based on the actual device.
int32_t deviceId = 0;
aclrtStream stream;
auto ret = Init(deviceId, &stream);
// Handle the check as needed.
CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
// 2. Construct the input and output. Customize the construction based on the API.
std::vector<int64_t> gradOutShape = {4};
// logProbsShape (T, N, C)
std::vector<int64_t> logProbsShape = {12, 4, 5};
std::vector<int64_t> targetsShape = {4, 7};
std::vector<int64_t> negLoglikelihoodShape = {4};
// logAlphaShape (N, T, X) X = ((max(targetLengths) * 2 + 1) + 7) / 8 * 8;
std::vector<int64_t> logAlphaShape = {4, 12, 16};
std::vector<int64_t> outShape = {12, 4, 5};
void* gradOutDeviceAddr = nullptr;
void* logProbsDeviceAddr = nullptr;
void* targetsDeviceAddr = nullptr;
void* negLoglikelihoodDeviceAddr = nullptr;
void* logAlphaDeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* gradOut = nullptr;
aclTensor* logProbs = nullptr;
aclTensor* targets = nullptr;
aclIntArray* inputLengths = nullptr;
aclIntArray* targetLengths = nullptr;
aclTensor* negLoglikelihood = nullptr;
aclTensor* logAlpha = nullptr;
aclTensor* out = nullptr;
std::vector<float> gradOutHostData = {1, 1, 1, 1};
std::vector<float> logProbsHostData = {
-1.0894, -2.7162, -0.9764, -1.9126, -2.6162,
-2.0684, -2.4871, -2.0866, -1.7205, -0.7187,
-2.4423, -1.2017, -1.4653, -1.1821, -2.5942,
-2.4670, -2.7257, -1.4135, -2.1042, -0.7248,
-3.7759, -1.3742, -1.2549, -1.5807, -1.4562,
-1.3826, -1.8995, -1.8527, -0.9493, -2.8895,
-1.6316, -2.6603, -2.5014, -0.6992, -1.8609,
-1.9269, -2.2350, -0.8073, -1.8906, -1.8947,
-0.3468, -2.5855, -2.0723, -2.7147, -3.6668,
-0.9541, -1.7258, -2.0693, -1.6378, -2.1531,
-3.5386, -3.4830, -0.2532, -2.0557, -3.3261,
-1.1480, -1.8080, -0.8244, -3.2414, -3.1909,
-0.8866, -0.7540, -4.4312, -3.4634, -2.6000,
-1.2785, -1.8347, -3.3122, -0.7620, -2.8349,
-1.4975, -1.3865, -0.9645, -3.8171, -2.0939,
-2.3536, -2.0773, -1.4981, -0.8372, -2.0938,
-1.2186, -0.8285, -2.9399, -2.1159, -2.3620,
-2.3139, -0.6503, -2.7249, -1.2340, -3.7927,
-0.7143, -2.5084, -3.2826, -2.6651, -1.1334,
-1.6965, -1.9728, -2.3849, -1.6052, -0.9554,
-1.6384, -1.2596, -2.1680, -1.8476, -1.3866,
-3.0455, -0.5737, -2.5339, -2.1118, -1.6681,
-2.4675, -2.8842, -0.4329, -3.6266, -1.6925,
-3.1023, -2.7696, -1.2755, -0.6470, -2.4143,
-2.0107, -2.0912, -1.3053, -0.8557, -3.0683,
-1.2872, -3.6523, -1.6703, -2.7596, -0.8063,
-2.4633, -1.2959, -1.6153, -2.3072, -1.0705,
-3.0543, -0.6473, -1.1650, -2.9025, -2.7710,
-3.5519, -2.0400, -1.8667, -1.4289, -0.8050,
-1.4602, -0.7452, -1.5754, -3.1624, -3.1247,
-1.4677, -1.2725, -2.9575, -1.8883, -1.2513,
-1.2164, -1.5894, -2.2217, -2.3714, -1.2110,
-2.0843, -0.6515, -1.4252, -2.9402, -2.7964,
-1.5261, -2.5471, -1.7167, -1.9846, -0.9488,
-1.4847, -1.7093, -1.4095, -1.7293, -1.7675,
-0.9203, -4.2299, -1.8740, -1.4076, -1.6671,
-1.9052, -0.8330, -2.1839, -2.2459, -1.6193,
-2.9108, -1.2114, -1.4616, -1.7297, -1.4330,
-2.2656, -0.7878, -1.8533, -1.8711, -2.0349,
-2.2457, -2.1395, -1.4509, -0.7538, -2.6381,
-0.8078, -2.1054, -2.6703, -1.1108, -3.3867,
-1.7774, -1.8426, -1.9473, -1.3293, -1.3273,
-1.3490, -1.9842, -2.5357, -2.2161, -0.8800,
-1.5412, -1.8003, -2.7603, -0.8606, -2.0066,
-1.8342, -2.2741, -1.8348, -1.5833, -0.9877,
-3.5196, -2.3361, -0.9124, -0.9307, -2.5531,
-1.4862, -1.2153, -1.4453, -3.4462, -1.5625,
-2.6455, -1.4153, -1.3079, -1.1568, -2.2897};
std::vector<int64_t> targetsHostData = {
1, 2, 1, 1, 2, 4, 1,
2, 2, 2, 2, 2, 2, 3,
4, 2, 1, 4, 3, 1, 4,
4, 1, 4, 2, 2, 2, 3};
std::vector<float> negLoglikelihoodHostData = {10.1999, 16.1340, 14.9006, 9.3596};
std::vector<float> logAlphaHostData = {
-1.0894, -2.7162, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999,
-4.8653, -2.2842, -6.4921, -3.9711, -99999, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-5.2121, -4.7967, -2.6162, -4.1742, -4.3179, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-6.0987, -5.0438, -3.3957, -6.7671, -4.4369, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-7.3173, -5.5735, -4.4384, -6.1313, -5.5627, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-8.9557, -6.6720, -5.7981, -6.1973, -6.7523, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-10.9664, -8.6661, -7.4600, -6.3671, -7.7544, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-14.5183, -10.6106, -10.7501, -7.8722, -9.6961, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-16.6026, -11.2422, -12.0691, -9.1833, -9.8069, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-18.5078, -12.0705, -12.7846, -11.1988, -10.6593, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-2.0684, -2.0866, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
-3.4510, -3.2370, -3.4692, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-4.4051, -4.7144, -3.6073, -5.5385, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-5.6836, -7.1669, -4.6003, -6.7841, -6.8170, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-7.9975, -8.2040, -6.8402, -7.2185, -8.4212, -9.5419, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-11.0430, -9.9362, -9.6580, -8.8523, -10.0013, -10.6729, -12.5874,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-12.3302, -11.3209, -10.3815, -10.1533, -9.8642, -11.2589, -11.8226,-14.2577, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-13.7904, -12.5855, -11.5118, -11.1431, -10.7654, -11.2181, -12.2686,-13.3140, -15.7179, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-15.3165, -14.0400, -12.7439, -12.3341, -11.7695, -11.9899, -12.4443,-13.6840, -14.7536, -17.4346, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-18.2273, -15.2555, -15.4129, -13.2866, -14.2301, -12.6421, -14.4091,-13.6517, -16.2998, -16.1490, -20.3454, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-2.4423, -2.5942, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
-4.0739, -3.6831, -4.2258, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-7.6125, -6.4925, -6.7635, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-9.1100, -8.3040, -7.4232, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-9.8243, -9.0682, -7.7908, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-12.2918, -10.3758, -10.0124, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-14.7551, -11.3089, -11.9478, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-16.2228, -12.5289, -12.3528, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-17.7075, -14.2718, -13.2285, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-19.9731, -16.2750, -15.1923, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-2.4670, -0.7248, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
-4.3939, -2.4581, -2.6517, -2.9598, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-5.5419, -5.5142, -3.0051, -3.3784, -4.1078, -6.1507, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-7.8955, -6.9286, -5.2805, -4.5115, -5.3385, -5.0374, -8.5043,-7.6488, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-9.5920, -7.5617, -6.8010, -6.0444, -5.8452, -4.7597, -6.7031,-7.3228, -9.3453, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-12.6943, -9.8527, -9.5199, -8.2901, -8.3490, -6.6950, -7.7281,-5.8361, -10.3008, -10.6208, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-15.7486, -12.5670, -12.0336, -8.5306, -10.6803, -9.1337, -9.4448,-6.5472, -8.8790, -10.9199, -13.6751, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-16.9650, -13.7373, -12.7884, -10.0734, -9.6368, -9.2326, -9.8004,-8.6463, -7.6709, -10.9785, -12.0746, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-17.8853, -15.3655, -13.3814, -14.2154, -10.0586, -10.1583, -9.7039,-9.8935, -8.2713, -9.5090, -11.6105, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
-20.1310, -17.9262, -15.4983, -15.0687, -12.2888, -12.0440, -11.4581,-10.2538, -10.3368, -9.4675, -11.6393, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000};
std::vector<float> outHostData = {
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0
};
// Create gradOut aclTensor.
ret = CreateAclTensor(gradOutHostData, gradOutShape, &gradOutDeviceAddr, aclDataType::ACL_FLOAT, &gradOut);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create logProbs aclTensor.
ret = CreateAclTensor(logProbsHostData, logProbsShape, &logProbsDeviceAddr, aclDataType::ACL_FLOAT, &logProbs);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create targets aclTensor.
ret = CreateAclTensor(targetsHostData, targetsShape, &targetsDeviceAddr, aclDataType::ACL_INT64, &targets);
CHECK_RET(ret == ACL_SUCCESS, return ret);
std::vector<int64_t> inputLengthsSizeData = {10,10,10,10};
inputLengths = aclCreateIntArray(inputLengthsSizeData.data(), 4);
CHECK_RET(inputLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
std::vector<int64_t> targetLengthsSizeData = {2, 3, 1, 5};
targetLengths = aclCreateIntArray(targetLengthsSizeData.data(), 4);
CHECK_RET(targetLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
// Create negLoglikelihood aclTensor.
ret = CreateAclTensor(negLoglikelihoodHostData, negLoglikelihoodShape, &negLoglikelihoodDeviceAddr, aclDataType::ACL_FLOAT, &negLoglikelihood);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create logAlpha aclTensor.
ret = CreateAclTensor(logAlphaHostData, logAlphaShape, &logAlphaDeviceAddr, aclDataType::ACL_FLOAT, &logAlpha);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// Create the out aclTensor.
ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 3. Call the CANN operator library API. Replace it with the specific API.
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// Call the first-phase API of aclnnCtcLossBackward.
ret = aclnnCtcLossBackwardGetWorkspaceSize(gradOut, logProbs, targets, inputLengths, targetLengths, negLoglikelihood, logAlpha, 0, false, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLossBackwardGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
// Allocate device memory based on the workspaceSize computed by the first-phase API.
void* workspaceAddr = nullptr;
if (workspaceSize > 0) {
ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret;);
}
// Call the second-phase API of aclnnCtcLossBackward.
ret = aclnnCtcLossBackward(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLossBackward failed. ERROR: %d\n", ret); return ret);
// 4. (Boilerplate) Synchronously wait for task execution to complete.
ret = aclrtSynchronizeStream(stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);
// 5. Obtain the output out value and copy the result from the device memory to the host memory. Modify based on the specific API definition.
auto size = GetShapeSize(outShape);
std::vector<float> resultData(size, 0);
ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(float),
ACL_MEMCPY_DEVICE_TO_HOST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
for (int64_t i = 0; i < size; i++) {
LOG_PRINT("out result[%ld] is: %f\n", i, resultData[i]);
}
// 6. Release aclTensor and IntArray. Modify based on the specific API definition.
aclDestroyTensor(gradOut);
aclDestroyTensor(logProbs);
aclDestroyTensor(targets);
aclDestroyIntArray(inputLengths);
aclDestroyIntArray(targetLengths);
aclDestroyTensor(negLoglikelihood);
aclDestroyTensor(logAlpha);
aclDestroyTensor(out);
// 7. Release device resources. Modify based on the specific API definition.
aclrtFree(gradOutDeviceAddr);
aclrtFree(logProbsDeviceAddr);
aclrtFree(targetsDeviceAddr);
aclrtFree(negLoglikelihoodDeviceAddr);
aclrtFree(logAlphaDeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}